Optimizing digital video distribution

ABSTRACT

Systems, methods, and apparatus include computer programs encoded on a computer-readable storage medium, including a system for ranking videos. Videos are identified that have been presented at client devices. For each video, session start data is identified that specifies a lead video that initiated presentation to a user during a presentation session. For each lead video, presentation times over multiple user sessions are determined, a scaled presentation time is obtained, user sessions for which the lead video initiated presentation of videos are identified, and an aggregate video presentation time attributable to the lead video is determined. For each given video, a presentation score is determined based on a scaled presentation time of the lead video relative to a sum of the aggregate video presentation times for the lead videos. The videos are ranked based on the presentation scores. A user interface is updated to present the ranked videos.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of, and claims priorityto, U.S. patent application Ser. No. 16/422,193, titled “OPTIMIZINGDIGITAL VIDEO DISTRIBUTION,” filed on May 24, 2019, which is acontinuation application of, and claims priority to, U.S. patentapplication Ser. No. 15/240,861, now U.S. Pat. No. 10,346,417 titled“OPTIMIZING DIGITAL VIDEO DISTRIBUTION,” filed on Aug. 18, 2016. Thedisclosure of each of the foregoing applications are incorporated hereinby reference in their entirety for all purposes.

BACKGROUND

This document relates to digitized video distribution.

Video-sharing services provide a platform for video content creators todistribute digitized videos to other parties over a network, such as theInternet. The video-sharing service may be implemented by a computingsystem comprising one or more servers in one or more locations. Contentcreators can upload their videos to the computing system, along withmetadata that describes their videos, which the computing system uses toindex the videos and make them discoverable to users who express aninterest in viewing such videos. The video-sharing service can thenreceive search queries from users requesting video content. In someinstances, the video-sharing service provides videos that have beenexplicitly recommended through social messaging systems.

SUMMARY

This document generally describes systems, methods, devices, and othertechniques for creating presentation scores and ranking videos based ondetermining lead videos that are attributed to presentation sessions. Asused throughout this document, the terms optimize and optimization donot refer to a single optimal result. Rather, these terms refer toimprovements in operations related to distributing videos over acommunications network. For example, optimizations related todistribution of digital video can include improvements to rankingtechniques that reduce the amount of time required (or number of videospreviewed) for a user to be presented a given video.

In general, one aspect of the subject matter described in thisspecification can be implemented in systems that include one or moreprocessing devices and one or more storage devices. The storage devicesstore instructions that, when executed by the one or more processingdevices, cause the one or more processing devices to perform operationsincluding: identifying, by one or more servers, various videos that havebeen presented at various different client devices that are remotelylocated relative to one or more computing devices that distribute thevarious videos for an online video distribution service; for each of thevarious videos, identifying session start data of a user within theonline video distribution service, wherein the session start dataspecifies a lead video that initiated video presentation to the userduring a given presentation session; for each lead video: determiningvarious presentation times of the lead video over multiple usersessions, obtaining a scaled presentation time for each lead videopresentation, including applying a scaling factor to the determinedpresentation time for the lead video presentation, identifying the usersessions for which the lead video initiated presentation of videos to auser, and determining an aggregate video presentation time attributableto the lead video based on the scaled presentation time of the leadvideo for each of the identified user sessions and a total presentationtime of other videos during the identified user sessions; generating,for each given video among the various videos, a presentation scorebased on an amount of presentation time of the given video relative to asum of the aggregate video presentation times for the lead videos,wherein the presentation score for each lead video is generated based onthe scaled presentation time of the lead video relative to the sum ofthe aggregate video presentation times for the lead videos; ranking thevarious videos based on the presentation scores; and updating a userinterface of the online video distribution service to present at least aportion of the ranked videos at a client device according to theranking.

These and other implementations can each optionally include one or moreof the following features. The operations can further include: for eachlead video specified by the session start data for the various videos:classifying the lead video as one of an in-service initiated videopresentation or a remotely initiated video presentation, whereinapplying a scaling factor to the determined presentation time for thelead video presentation includes: applying a first scaling factor to thedetermined presentation time for in-service initiated videopresentations of a lead video; and applying a second scaling factor tothe determined presentation time for remotely initiated videopresentations of a lead video, wherein the first scaling factor is lowerthan the second scaling factor. The operations can further includeidentifying remotely initiated video presentations of a lead video basedon referrer information included in a request to present the lead video,wherein the referrer information specifies one of a third-party websitethat directed a user to the online video distribution service, athird-party native application that directed a user to the online videodistribution service, or shared link that directed a user to the onlinevideo distribution service. The operations can further includeidentifying search queries that resulted in a given lead video beingidentified to various users in search results; for each of the searchqueries, determining a portion of the various users that initiatedpresentation of the given lead video through interaction with the searchresults; and determining, based on the determined portions, searchscaling factors to apply to the determined presentation time of the leadvideo for the presentations of the given lead video that was initiatedthrough user interaction with the search results that identified thegiven lead video. The operations can further include identifying acreator that supplies one or more of the various videos. The operationscan further include generating a creator score for the creator based onthe presentation score of the one or more of the various videos providedby the creator; and ranking the creator among other creators based, atleast in part, on the creator score. The operations can further includedistributing, to the creator, a portion of proceeds attributable to theone or more of the various videos supplied by the creator based on thepresentation scores of the one or more of the various videos.

In general, another innovative aspect of the subject matter described inthis specification can be implemented in methods, including a methodthat includes: identifying, by one or more servers, various videos thathave been presented at various different client devices that areremotely located relative to one or more computing devices thatdistribute the various videos for an online video distribution service;for each of the various videos, identifying session start data of a userwithin the online video distribution service, wherein the session startdata specifies a lead video that initiated video presentation to theuser during a given presentation session; for each lead video:determining various presentation times of the lead video over multipleuser sessions, obtaining a scaled presentation time for each lead videopresentation, including applying a scaling factor to the determinedpresentation time for the lead video presentation, identifying the usersessions for which the lead video initiated presentation of videos to auser, and determining an aggregate video presentation time attributableto the lead video based on the scaled presentation time of the leadvideo for each of the identified user sessions and a total presentationtime of other videos during the identified user sessions; generating,for each given video among the various videos, a presentation scorebased on an amount of presentation time of the given video relative to asum of the aggregate video presentation times for the lead videos,wherein the presentation score for each lead video is generated based onthe scaled presentation time of the lead video relative to the sum ofthe aggregate video presentation times for the lead videos; ranking thevarious videos based on the presentation scores; and updating a userinterface of the online video distribution service to present at least aportion of the ranked videos at a client device according to theranking.

These and other implementations can each optionally include one or moreof the following features. The method can further include: for each leadvideo specified by the session start data for the various videos:classifying the lead video as one of an in-service initiated videopresentation or a remotely initiated video presentation, whereinapplying a scaling factor to the determined presentation time for thelead video presentation includes: applying a first scaling factor to thedetermined presentation time for in-service initiated videopresentations of a lead video; and applying a second scaling factor tothe determined presentation time for remotely initiated videopresentations of a lead video, wherein the first scaling factor is lowerthan the second scaling factor. The method can further includeidentifying remotely initiated video presentations of a lead video basedon referrer information included in a request to present the lead video,wherein the referrer information specifies one of a third-party websitethat directed a user to the online video distribution service, athird-party native application that directed a user to the online videodistribution service, or shared link that directed a user to the onlinevideo distribution service. The method can further include identifyingsearch queries that resulted in a given lead video being identified tovarious users in search results; for each of the search queries,determining a portion of the various users that initiated presentationof the given lead video through interaction with the search results; anddetermining, based on the determined portions, search scaling factors toapply to the determined presentation time of the lead video for thepresentations of the given lead video that was initiated through userinteraction with the search results that identified the given leadvideo. The method can further include identifying a creator thatsupplies one or more of the various videos. The method can furtherinclude generating a creator score for the creator based on thepresentation score of the one or more of the various videos provided bythe creator; and ranking the creator among other creators based, atleast in part, on the creator score. The method can further includedistributing, to the creator, a portion of proceeds attributable to theone or more of the various videos supplied by the creator based on thepresentation scores of the one or more of the various videos.

In general, another aspect of the subject matter described in thisspecification can be implemented a non-transitory computer storagemedium encoded with a computer program. The program can includeinstructions that when executed by a distributed computing system causethe distributed computing system to perform operations includingidentifying, by one or more servers, various videos that have beenpresented at various different client devices that are remotely locatedrelative to one or more computing devices that distribute the variousvideos for an online video distribution service; for each of the variousvideos, identifying session start data of a user within the online videodistribution service, wherein the session start data specifies a leadvideo that initiated video presentation to the user during a givenpresentation session; for each lead video: determining variouspresentation times of the lead video over multiple user sessions,obtaining a scaled presentation time for each lead video presentation,including applying a scaling factor to the determined presentation timefor the lead video presentation, identifying the user sessions for whichthe lead video initiated presentation of videos to a user, anddetermining an aggregate video presentation time attributable to thelead video based on the scaled presentation time of the lead video foreach of the identified user sessions and a total presentation time ofother videos during the identified user sessions; generating, for eachgiven video among the various videos, a presentation score based on anamount of presentation time of the given video relative to a sum of theaggregate video presentation times for the lead videos, wherein thepresentation score for each lead video is generated based on the scaledpresentation time of the lead video relative to the sum of the aggregatevideo presentation times for the lead videos; ranking the various videosbased on the presentation scores; and updating a user interface of theonline video distribution service to present at least a portion of theranked videos at a client device according to the ranking.

These and other implementations can each optionally include one or moreof the following features. The operations can further include: for eachlead video specified by the session start data for the various videos:classifying the lead video as one of an in-service initiated videopresentation or a remotely initiated video presentation, whereinapplying a scaling factor to the determined presentation time for thelead video presentation includes: applying a first scaling factor to thedetermined presentation time for in-service initiated videopresentations of a lead video; and applying a second scaling factor tothe determined presentation time for remotely initiated videopresentations of a lead video, wherein the first scaling factor is lowerthan the second scaling factor. The operations can further includeidentifying remotely initiated video presentations of a lead video basedon referrer information included in a request to present the lead video,wherein the referrer information specifies one of a third-party websitethat directed a user to the online video distribution service, athird-party native application that directed a user to the online videodistribution service, or shared link that directed a user to the onlinevideo distribution service. The operations can further includeidentifying search queries that resulted in a given lead video beingidentified to various users in search results; for each of the searchqueries, determining a portion of the various users that initiatedpresentation of the given lead video through interaction with the searchresults; and determining, based on the determined portions, searchscaling factors to apply to the determined presentation time of the leadvideo for the presentations of the given lead video that was initiatedthrough user interaction with the search results that identified thegiven lead video. The operations can further include identifying acreator that supplies one or more of the various videos. The operationscan further include generating a creator score for the creator based onthe presentation score of the one or more of the various videos providedby the creator; and ranking the creator among other creators based, atleast in part, on the creator score.

In some implementations, the techniques described herein may realize, incertain instances, one or more of the following advantages. Relevantvideos can be surfaced with fewer clicks, less data transfer, andreduced search time. Videos that are associated with a user's intent towatch, such as specific videos identified by a friend, can be rankedhigher for presentation than videos that are merely recommended by anonline video distribution service. As such, a more optimized videodistribution system is provided.

DESCRIPTION OF DRAWINGS

FIG. 1 illustrates a conceptual diagram of video presentation and theuse of scaling factors to determine presentation scores for videos.

FIG. 2 depicts a block diagram of an example environment in which videosare presented and scaling factors are used to determine scaledpresentation times and presentation scores for videos.

FIG. 3 shows a block diagram of an example computing system, including avideo search system and a video storage system, in which videos arepresented and scaling factors are used to determine scaled presentationtimes for videos.

FIG. 4 depicts a flowchart of an example process in which videos arepresented and scaling factors are used to determine scaled presentationtimes for videos.

FIG. 5 shows an example of a computing device and a mobile computingdevice that may be used to implement the computer-implemented methodsand other techniques described herein.

Like references among the various drawings indicate like elements.

DETAILED DESCRIPTION

This document generally describes systems, methods, devices, and othertechniques for creating presentation scores and ranking videos based ondetermining lead videos that are attributed to presentation sessions.For example, a lead video can be a video that is identified asinitiating or leading (e.g., presented first within) a givenpresentation session, such as a specific video selected and initiatedfrom a link outside of an online video distribution service. Rankinglead videos higher, for example, can optimize the presentation and/orsuggestion of videos by surfacing videos that actually direct (e.g.,start presentation sessions on) the online video distribution service.For example, videos that have brought a user to an online videodistribution service can be ranked higher than videos that are simplyrecommended by the online video distribution service after the user isalready interacting with the video distribution service. This means thata specific video recommended by another user on a social messagingsystem can be ranked higher than popular videos that are selected by theonline video distribution service as relevant to a given search query.

A computing system may implement the online video-sharing service thathosts videos submitted by a collection of independent creators and makesthe creators' videos available for distribution over a network (e.g.,the Internet) to one or more viewers. Viewers may access thevideo-sharing service on respective client devices and search videosthat that are hosted on the service. The system may also present toviewers recommendations likely to be of interest to the viewers.

In some implementations, the ranking and other aspects described in thisdocument can be used as a ranking mechanism for user-generated content(UGC) as well as a mechanism for distributing compensation among videocreators that provided videos viewed over the course of a presentationsession. For example, the rankings of videos presented during a givenpresentation session (or across multiple presentation sessions) can beadjusted disproportionately based on which of the presented videosbrought a user to the online video distribution service and which of thepresented videos were recommended to the user after the user arrived atthe video distribution service. In some implementations, the source of apresentation session can be identified, such as by identifying the firstvideo presented during a given presentation session as the lead video ofthe presentation session. Subsequently, when a video identified as alead video is selected to appear in search results, the rank of thevideo can be increased. Similar, compensation provided to creators oflead videos can utilize a similar disproportionate adjustment mechanism,as described below.

Ranking of videos can be based, at least in part, on presentation scoresthat are determined for each video. For example, the presentation scoreof a given video can be based on an amount of presentation time of thegiven video relative to a sum of the aggregate video presentation timesfor lead videos, or relative to a sum of presentation times for videosin a same presentation session. However, for videos that are determinedto be lead videos, each lead video can be assigned a presentation scorethat reflects the lead video's contribution to a given presentationsession that is attributable to the lead video. Further, thepresentation score for the lead video can be affected by other leadvideos. For example, the presentation score for the lead video can begenerated based on a scaled presentation time of the lead video relativeto the sum of the aggregate video presentation times for the leadvideos. The scaled presentation time for the lead video can be afunction of the determined (e.g., actual) presentation time of the videoand a scaling factor. Scaling factors (e.g., greater than 1) that areapplied to lead videos, for example, can result in a higher presentationscore for (and subsequent higher ranking of) a lead video relative tonon-lead videos.

FIG. 1 illustrates a conceptual diagram of video presentation and theuse of scaling factors to determine presentation scores for videos. Forexample, a presentation session 102 (e.g., presented left-to-right overtime 103) includes a sequence of presented videos 104 and a search 105on a video site, such as a web site of an online video distributionservice. The presented videos 104 have associated presentation times106, such as presentation times 106 a-106 c of five minutes for each ofthe presented videos 104 a-104 c, and a presentation time 106 d of tenminutes for presented video 104 d.

In one example sequence of events, a user using a social messagingsystem (e.g., reading a friend's post) can click on a link for thepresented video A 104 a created by Creator 1. This can cause initiationof the presentation session 102, such as a presentation session on theonline video distribution service. For example, clicking the link canautomatically initiate opening, by a client device, a video applicationassociated with the online video distribution service and initiating theplaying/streaming of video A 104 a. After video A 104 a is presented forfive minutes, for example, the user may be presented with one or moreother videos for an additional 20 minutes, including videos selected bythe user from related recommended videos uploaded by various creatorsdifferent from Creator 1. In the example shown in FIG. 1, the additional20 minutes can start with video B 104 b, which may also be referred fromthe same social messaging system from which video A 104 a was referred.Assume that the next video to be presented is video C 104 c, e.g., avideo recommended by the online video distribution service. During thesame presentation session, the user may perform a search 105 on thevideo site, from which video D 104 d, a video selected form theresulting search results, is selected for presentation. In this example,videos C 104 c and D 104 d are assumed not to be associated with (e.g.,provided by) Creator 1.

Video A 104 a can be determined to be a lead video attributable to thepresentation session 102, e.g., being the reason that the presentationsession 102 was initiated (and that the other videos were presented). Inorder to recognize the contribution of the lead video in this example, apresentation score for the lead video can be determined based, at leastin part, on a scaling factor associated with the lead video. Forexample, a scaling factor of 2 (or some other value) can be applied tothe five-minute presentation time of video A 104 a, e.g., resulting in ascaled presentation time, e.g., ten minutes. The ten-minute scaledpresentation time can be added to the total (e.g., 40 minutes) ofpresentation times of other videos, resulting in an aggregate videopresentation time 111 of 50 minutes for videos in the presentationsession.

Scaling factors 108 of 2, 2, 1 and 2.5 can be determined for the Videos104 a-104 d, respectively. Using the scaling factors 108, correspondingscaled presentation times 110 can be determined, e.g., scaledpresentation times 110 of 10, 5 and 25 minutes that are associated withvideos 104 having scaling factors 108 of 2, 1 and 2.5, respectively.

In some implementations, during the course of a presentation session,multiple videos can be marked as the lead video. For example, if video B104 b has the same author (e.g., Creator 1) as video A 104 a and ispresented immediately after (or in the same presentation session as)video A 104 a, then video B 104 b can also be identified as a lead videowithin the presentation session 102. The scaling factor 108 for video B104 b can be the same, or can be slightly lower than, the scaling factor108 for video A 104 a. As a result, multiple lead videos can be definedfor one or more presentation sessions associated with the user during a24-hour period, e.g., within a same presentation session or during anyperiod that is defined by the user's arrival and subsequent departure(or determined inactivity) on the online video distribution service.

In some implementations, a lead video's scaling factor can be applied invarious ways. For example, in a simplistic model approach, a constantscaling factor can be applied to the presentation time of only the videoitself, such as two times in the above example. In some implementations,an additive constant (e.g., five minutes) can be used that incentivizesshort videos that initiate longer presentation sessions as opposed toonly incentivizing long videos. In some implementations, a scalingfactor can be applied to all videos associated with the creator of alead video, e.g., if the videos are presented during the presentationsession, thus rewarding the creator for the creator's total presence inthe presentation session rather than just for the first video. In someimplementations, variable scaling factors can depend on the source ofthe lead video. For example, the scaling factor for a video associatedwith a deep link (e.g., a hypertext link other than a home page) can begreater than the scaling factor for a video associated with a link for ahome page, which in turn can be greater than the scaling factor for avideo identified from a search.

The source of the lead video can be classified in various ways. Forexample, a first-party navigation classification can be used when theuser navigates using a first-party action such as following anotification, recommendation, first-party share, or other user action.The “first-party” nomenclature in these examples refers to the userhaving a direct relationship with another user (e.g., who recommends aspecific video) or an explicit action by the user outside of the onlinevideo distribution service. Each of these different types of navigationcan be classified differently, e.g., resulting in different scalingfactors being applied. A web referrer classification, for example, canbe used if the referrer (e.g., a user on a social messaging system) canbe identified so that a determination can be made as to which web sitethe user has come from in order to be presented videos on the onlinevideo distribution service. In some implementations (e.g., some mobilenative environments), the referrer can be identified so as to identifythe website or application from which the user originated. In someimplementations, identification of the originating website orapplication can be achieved by the referring service by adding extrametadata to a URL, or by providing referrer (e.g., application intents)to the URL to provide context about the originating web site orapplication.

In some implementations, attribution tags (e.g., using bundled URLs) canbe used to mark shared links so that downstream views can be attributedto a sharer of the video. Attribution tags can also be used, forexample, as a proxy for application or website information where noneexists.

In some implementations, scaling factors for presentation times can beadjusted based on the total amount of time the user was presentedvideos, such as to generate higher presentation scores for short videosthat lead to long presentation sessions. For example, a scaling factorcan be based on be a sliding scale between a 300% and 120% scalingfactor between 5 minutes and 30 minutes. In this way, short videos thatstart long presentation sessions can be valued (e.g., using scalingfactors) similarly to long videos that generate similarly longpresentation sessions. Scaling factors can be used to generatepresentation scores for videos, which can affect the order ofpresentation of videos in search results.

Search results might be divided into different types that each affectthe source scaling factor in a different way. For example, an expresssearch (e.g., using a specific title) for a particular video or channelcan have a very high source scaling factor. A general search (e.g.,using keywords in a search query) can have a very low source scalingfactor because the user was presented with the video that was deliveredin search results (as opposed to it being the specific video identifiedby title). The specificity of a video search can be determined, forexample, by looking at a percentage of users who clicked on a searchresult from the channel. In some implementations, if the majority ofusers clicked on the same video or videos uploaded by the same creatorfor a particular search term, then searches with that term can beconsidered to be an explicit search.

Video creators can be compensated for bringing users to an online videodistribution service. For example, video creators can be compensatedmore if their videos cause the start of a presentation session even if amajority of video presentation time in the presentation session comes isassociated with videos that are recommend by the online videodistribution service. In this way, top-tier creators whose video contentbrings users to the online video distribution service can be compensatedmore than creators whose videos are presented as a result of on-sitevideo recommendations.

In some implementations, content creators are not paid just for thesponsored content that show up on their videos, but additionally (orinstead) can be paid based on the value that they bring to the onlinevideo distribution service or the viewer. For example, the revenue froma presentation session on a video sharing site can be totaled and thendivided among the video creators based on a presentation scores of eachvideo within the presentation session.

FIG. 2 depicts a block diagram of an example environment 200 in whichvideos are presented and scaling factors are used to determine scaledpresentation times and presentation scores for videos. A datacommunication network 202 enables data communication between multipleelectronic devices and systems. In this environment 200, users canaccess video content, provide video content, exchange information, andsearch for videos over the data communication network 202. The datacommunication network 202 can include, for example, a local area network(LAN), a cellular phone and data network, a wide area network (WAN),e.g., the Internet, or a combination of these and other networks. Thelinks on the network can be wireline or wireless links or both. Videocontent creators can also generate and create videos to share over thedata communication network 202 using video creator systems 226.

In some implementations, the environment 200 includes a video searchsystem 210. In operation, the video search system 210 can implement avideo-sharing service (e.g., an online video distribution service) thatenables various parties (creators) to make digitized videos availablefor online distribution to other parties (viewers). The video-sharingservice may be accessed from a website or applications associated withone or more domains. For example, parties may upload original videocontent to the video search system 210, and the video search system 210can store and index the videos in the video storage system 211. Viewersat viewer systems 218 may then query the video search system 210 torequest video content according to one or more preferences of theviewers. In some implementations, the video search system 210 isconfigured to provide video content to users in which selection andmonetization of the videos depends on the application of scaling factorsto presentation times. The details of the video search system 210 andvideo storage system 211 are described further herein with respect toFIG. 3.

Generally, each viewer can access videos through a viewer system 218associated with the respective viewer. A given viewer system 218 caninclude an electronic device, or collection of devices, capable ofrequesting, receiving, and playing videos over the data communicationnetwork 202. Example viewer systems 218 may include one or more of asmartphone, a tablet computing device, a notebook computer, a desktopcomputers, a smart television device, a wearable computing device, avirtual reality device, an augmented reality device, or a combination oftwo or more of these. The viewer system 218 may include a userapplication, e.g., a web browser or a native media player applicationthat sends and receives data over the data communication network 202,generally in response to user actions. The web browser can enable a userto display and interact with text, images, videos, music and otherinformation typically located on a web page at a website on the Internetor a local area network. The media player application may play digitizedvideos downloaded or streamed from the video search system 210, and maygenerate presentation time reports that are transmitted back to thevideo search system 210 to identify how viewers were presented withserved videos on the viewer systems 218.

In some implementations, the environment 200 can include a publisherwebsite 204 includes one or more resources 205 associated with a domainand hosted by one or more servers in one or more locations. Generally, awebsite is a collection of web pages formatted in hypertext markuplanguage (HTML) that can contain text, images, multimedia content (e.g.,videos), and programming elements, for example, scripts. Each publisherwebsite 204 is maintained by a content publisher, which is an entitythat controls, manages and/or owns the publisher website 204. Thepublisher websites 204 can provide a variety of different web pages,such as web pages that present videos hosted by the video-sharingservice at video search system 210.

A resource 205 in this context can include any data that is provided bythe publisher website 204 over the data communication network 202 andthat has a resource address, e.g., a uniform resource locator (URL).Resources may be HTML pages, electronic documents, images files, videofiles, audio files, and feed sources, to name just a few. The resourcesmay include embedded information, e.g., meta information and hyperlinks,and/or embedded instructions, e.g., client-side scripts.

In some implementations, the environment 200 can include a content itemmanagement system 220, which generally provides content items (e.g.,advertisements) for presentation with videos that the video searchsystem 210 serves to viewer systems 218. In some implementations, thecontent item management system 220 allows content providers to defineselection rules that take into account characteristics of a particularvideo viewer to provide relevant content to the viewer. Exampleselection rules include keyword selection, in which the contentproviders specify content selection criteria for content associated withkeywords that are present in either search queries, videos, or videocontent metadata.

The content item management system 220 can include a data storage systemthat stores campaign data 222 and performance data 224. The campaigndata 222 can store, for example, content items, selection information,and budgeting information for content providers. The performance data224 can store data indicating the performance of the content items thatare served. Such performance data can include, for example,click-through rates for content items, the number of impressions forcontent items, and the number of resulting conversions associated withthe impressions.

In some implementations, the campaign data 222 and the performance data224 can be used as input to a content item selection procedure. Inparticular, the content item management system 220, in response to eachrequest for content, conducts a selection procedure to select items thatare provided in response to the request. The content item managementsystem 220 can rank content items according to a score that, in someimplementations, is proportional to a value based on a content itempassed on performance data 224.

Turning to FIG. 3, a block diagram is shown of an example computingsystem 300, including a video search system 302 and a video storagesystem 304, in which videos are presented and scaling factors are usedto determine scaled presentation times for videos. In someimplementations, the video search system 210 of FIG. 2 may beimplemented as the video search system 302 of FIG. 3. In someimplementations, the video storage system 211 may be implemented as thevideo storage system 304 of FIG. 3. In some implementations, thecomputing system 300 may further be configured to carry out the process400, which are represented by the flowchart in FIG. 4. Each of the videosearch system 302 and the video storage system 304 may be implemented onone or more computers in one or more locations. The computers of thesystems 302 and 304 may be implemented by a combination of software andhardware as depicted and described with respect to FIG. 5.

In some implementations, the video search system 302 provides an onlinevideo-sharing service in which various parties can upload digitizedvideos to the service to make the videos available for distribution toone or more other parties. For the purpose of this document, the partiesthat submit (e.g., upload) videos for distribution through the serviceare referred to as creators, and the parties that are presented withvideos through the service are referred to as viewers. In many cases,“creators” may include parties that organically created their own videosto share with others, but “creators” may also refer to parties whoupload content that was actually created by one or more other partiesbut which the first party wishes to share on the service. The videosearch system 302 may include a creator platform 328 that provides aninterface for creators to submit and monitor the performance of theirvideos on the sharing service. Creators may register with accounts 332with the service and may use various tools 330 to facilitate videocontent creation and distribution.

In some implementations, the video search system 302 may enforcepolicies and technological restrictions to prevent parties fromdistributing content through the video-sharing service without properauthorization from the original creator of the content. The video searchsystem 302 is generally operable to select video content to provide asrecommendations to users or as responses to search queries submitted byusers, based at least in part on presentation time information thatindicates how long users in different viewer categories have vieweddifferent videos. In some implementations, viewers can stream digitizedvideos hosted by the computing system 300. In some implementations,viewers can download all or portions of digitized videos hosted by thecomputing system 300 to allow the viewers to watch the videos offline atlater times, for example.

The video storage system 304 is generally responsible for storing,maintaining, and indexing video content for videos that have been madeavailable for distribution on the video-sharing service. The videostorage system 304 can include a video content repository 334 and anindex 336. The video storage system 304 includes one or more processorsand one or more storage devices in one or more locations that storevideo content for a large number of digitized videos. For example, whena creator uploads a video to the video search system 302 for sharing,the video file can be provided to the video storage system 304,processed (e.g., compressed and made to conform to one or more standardresolutions), stored, and indexed for searching. Generally, videocontent may include the actual digitized video itself as well aspertinent metadata about the digitized video. For example, the videocontent repository 334 may identify a title, a short textualdescription, and a creator ID for a given video, and correlate themetadata with the digitized video file in the video storage system 304.The index 336 includes information that makes the video contentsearchable, such as references to the identified metadata for variousvideos, hash tables, or the like. The video storage system 304 and thevideo search system 302 can pass messages between each other to identifyand provide video content that is to be served to computing devicesseparate from the computing system 300 (e.g., over the Internet).

In some implementations, the video search system 302 can include apresentation scoring apparatus 305, a presentation time modelingapparatus 306, a video content selector 316, a viewer profile manager318, a network interface 324 (e.g., front-end server), a request manager326, a creator platform 328, or a combination of all or some of thesecomponents. Each of the components may generally be implemented as acombination of hardware and software of one or more computers in one ormore locations, such as computers described with respect to FIG. 5.

The network interface 324 is generally configured to enable networkcommunications for the video search system 302. The network interface324 can receive from creators' computing devices requests to makedigitized video content available for distribution on the sharingservice provided by the video search system 302. The network interface324 can also receive from viewers' computing devices requests to provideshared video content for presentations to the viewers and can servevideo content to the viewers' computing devices responsive to theirrequests.

The request manager 326 is generally configured to process requestsreceived from computing devices remote from the video search system 302,as indicated by the network interface 324. For requests for videocontent from viewers' computing devices, the request manager 326 cananalyze the request to identify one or more selection criteria for videocontent that is to be served to the viewers' computing devicesresponsive to the requests. The selection criteria may be expresslyindicated in the content of a given request and/or the selectioncriteria may be identified from data sources external to the request,based on information associated with the request. As an example, somerequests may expressly include a search query that identifies one ormore terms entered and submitted by a viewer, where the terms indicatetopics of video content that the user has targeted for a search.

Some requests, however, may not include a search query or otherwise maynot expressly identify topics of video content that has been requested.The request manager 326 may nevertheless identify topics or otherselection criteria for the request based on circumstantial data ormetadata associated with the request, such as an identity of the user towhom the video content is to be presented, a timestamp assigned to therequest indicating a time that the request was submitted, or locationinformation that indicates a location of the user to whom the requestedvideo content is to be submitted. For example, the request manager 326can identify the targeted viewer based on analysis of the request, andthen one or more characteristics of the user can be identified from theviewer profile manager 318 and used by the video content selector 316 asselection criteria for determining video content to serve in a responseto the request. A video content request may not include a query, forexample, when the video search system 302 is requested to provide arecommendation for video content to a user, such as when the user firstaccesses a homepage of the video-sharing service and before the user hasentered a query, so that the user is automatically presented withoptions for viewing digitized videos of potential interest to the usermerely by virtue of having visited the homepage.

The presentation scoring apparatus 305 is generally configured togenerate presentation scores for various videos in a presentationsession based on an amount of presentation time of a given videorelative to a sum of aggregate video presentation times for videos inthe presentation session. For example, each presentation score candepend on an aggregate video presentation time for the videos in thepresentation session. Videos that are determined to be lead videos inthe presentation session, for example, can have scaled presentationtimes that are increased (e.g., higher than non-lead videos) based onthe application of scaling factors.

A lead video identifier/classifier 307 can perform operations associatedwith identifying and classifying lead videos. For example, for each leadvideo, the lead video identifier/classifier 307 can use information froma presentation time data repository 308 to determine variouspresentation times of the lead video over multiple presentationsessions. The lead video identifier/classifier 307 can obtain a scaledpresentation time for each lead video presentation, including applying ascaling factor (e.g., obtained from a scaling factor engine 309) to thedetermined presentation time for the lead video. The lead videoidentifier/classifier 307 can also identify the presentation sessionsfor which the lead video resulted in the presentation of a group ofvideos to a user. Using scaling factors obtained from the scaling factorengine 309, the lead video identifier/classifier 307 can determine anaggregate video presentation time attributable to the lead video basedon the scaled presentation time of the lead video for each of theidentified presentation sessions and a total presentation time of othervideos during the identified presentation sessions.

The scaling factor engine 309 can generate, for each given video amongthe various videos, a presentation score based on an amount ofpresentation time of the given video relative to a sum of the aggregatevideo presentation times for the lead videos. The presentation score foreach lead video can be generated based on the scaled presentation timesof the lead video relative to the sum of the aggregate videopresentation times for the lead videos. The scaling factor engine 309can rank the various videos based on the presentation scores. The videosearch system 302 can update a user interface of the online videodistribution service to present at least a portion of the ranked videosat a client device according to the ranking.

In some implementations, the presentation scoring apparatus 305 can useinformation and services of the lead video identifier/classifier 307 andthe scaling factor engine 309 to apply various scaling factors thatdepend on the source of the lead video. For example, for each lead videospecified by the presentation session start data for the various videos,the lead video identifier/classifier 307 can classify the lead video asone of an in-service initiated video presentation (e.g., selected by theuser from a list of recommendations) or a remotely initiated videopresentation (e.g., through a specific link to a video provided by afriend). The scaling factor engine 309 can apply a scaling factor to thedetermined presentation time for the lead video in various ways. Forexample, a first (e.g., lower) scaling factor can be applied to thedetermined presentation time for in-service initiated videopresentations of a lead video, and a second (e.g., higher) scalingfactor can be applied to the determined presentation time for remotelyinitiated video presentations of a lead video. In some implementations,the 302 can distribute, to the creator of a video, a portion of proceedsattributable to the one or more of the various videos supplied by thecreator based on the presentation scores of the one or more of thevarious videos.

A search query analysis engine 311 can generate and use search scalingfactors that are applied to presentation times based on search queriesthat are the source of lead videos. For example, the search queryanalysis engine 311 can identify search queries that resulted in a givenlead video being identified to various users in search results. For eachof the search queries, the search query analysis engine 311 candetermine a portion of the various users that initiated presentation ofthe given lead video through interaction with the search results. Thescaling factor engine 309 can determine, based on the determinedportions, search scaling factors to apply to the determined presentationtime of the lead video for the presentations of the given lead videothat was initiated through user interaction with the search results thatidentified the given lead video.

A video creator analysis engine 313 can perform operations associatedwith identifying and characterizing video creators. For example, thevideo creator analysis engine 313 can identify a creator that suppliedone or more of the various videos, generate a creator score for thecreator (e.g., based on the presentation score of the one or more of thevarious videos provided by the creator), and rank the creator amongother creators based, at least in part, on the creator score.

The presentation time modeling apparatus 306 is generally configured todetermine models for scoring videos, creators, or both based onpresentation time data that identifies how long various viewers havingdifferent characteristics have viewed various digitized videos hosted bythe video search system 302 (and stored on the video storage system304). As with other components and sub-components of the video searchsystem 302, the presentation time modeling apparatus 306 can compriseone or more computers in one or more locations, including one or moreprocessors and/or one or more computer-readable storage devices. Thepresentation time modeling apparatus 306 can include a presentation timedata repository 308, an instrumentation engine 310, one or morepresentation time models 312, one or more creator performance models314, or a combination of these.

In some implementations, the presentation time modeling apparatus 306can also use information in the presentation time data repository 308 toidentify various videos that have been presented at various differentclient devices that are remotely located relative to one or morecomputing devices that distribute the various videos for an online videodistribution service. For each of the various videos, the presentationtime data repository 308 can identify presentation session start data ofa user within the online video distribution service. For example, thepresentation session start data can specify a lead video that initiatedvideo presentation to the user during a given presentation session.

Using information from the presentation time data repository 308, thepresentation time modeling apparatus 306 can identify remotely initiatedvideo presentations of a lead video based on referrer informationincluded in a request to present the lead video, wherein the referrerinformation specifies one of a third-party website that directed a userto the online video distribution service, a third-party nativeapplication that directed a user to the online video distributionservice, or shared link that directed a user to the online videodistribution service.

The presentation time data repository 308 stores presentation time data,received at the network interface 324, which identifies how long variousviewers have viewed videos hosted and presented by the video searchsystem 302. In some implementations, the presentation time datarepository 308 can include a database that logs presentation timereports provided to the video search system 302 from respective viewers'computing devices. Each time a presentation time report is received froma viewer's computing device, an entry can be added or updated in thedatabase of the presentation time data repository 308. In someimplementations, video playback applications on viewer's computingdevices can be configured to automatically generate and providepresentation time reports to the video search system 302 as the vieweris presented a given video and/or after the viewer has completedwatching all or a portion of a given video.

For example, when a viewer begins playing a video, the video playbackapplication can automatically transmit a timestamp indicating a starttime of the video to the video search system 302, which is logged in thedatabase. As the viewer continues to be presented with the video, thevideo playback application can periodically (e.g., every 1 second orless frequently) ping the video search system 302 to confirm that theuser is continuing to play the video. When the viewer stops playing thevideo, the video playback application can send a message to the videosearch system 302 that indicates the viewer has stopped the video, andthe message can be logged in the database to indicate the totalpresentation time of the video by the viewer in a presentation session.In some implementations, the video playback application may store logsof one or more videos a user viewed and respective presentation times ofthe videos over a period of time (e.g., over a browsing session, anhour, a day, a week, a month, a year, etc.). The stored logs can then betransmitted to the video search system 302 on a regular basis, and thepresentation time modeling apparatus 306 can register the logged data inthe presentation time data repository 308. In some implementations, thepresentation time data repository 308 may thus include data thatidentifies a plurality of different digitized videos, and for eachrespective video, a respective presentation time of the video by each ofa plurality of viewers.

In some implementations, the presentation time modeling apparatus 306can include an instrumentation engine 310 (or the instrumentation enginemay be a separate component of the video search system 302). Theinstrumentation engine 310 is generally configured to generate andinject executable code or other instructions into web pages orapplications associated with the playback of videos on client devices tocause the client devices to report presentation time data back to thevideo search system 302. For example, the instrumentation engine 310 mayinsert a script into a web page that presents a video, and when thescript is executed in a web browser at the client computing device, thescript monitors the status of the video being played on the clientdevice and logs presentation time data. The script can thenasynchronously report presentation time information to the video searchsystem 302.

The presentation time modeling apparatus 306 is further operable togenerate one or more presentation time models 312 using information fromthe presentation time data repository 308 and information about theviewers whose presentation time is reflected in the presentation timedata repository 308. In some implementations, the presentation timemodeling apparatus 306 can access or otherwise obtain information aboutviewers from the viewer profile manager 318. The viewer profile manager318 is generally operable to assign unique identifiers to viewers and tocorrelate information about one or more characteristics of viewers withtheir respective identifiers. In some implementations, the viewercharacteristics can generally be classified into one of two categories,namely demographic characteristics and behavioral characteristics. Thedemographic characteristics may encompass personal characteristics ofthe viewer (e.g., age group, gender) and/or external characteristics ofthe viewer, such as a geographic location where the video was presented,the time of day that the video was presented, a type of computing deviceon which the video was presented, a video playback application that wasused when the video was presented, a browser application used forpresenting the video, and the viewer's network connection bandwidth).

The behavioral characteristics generally relate to actions the viewertakes in connection with being presented with a particular video. Oneexample of a behavioral characteristic is a set of one or more queriesthat the user submitted to the video search system and/or to anothercomputing system (e.g., a general search engine) during a presentationsession that led to the viewer being presented with a particular video.For example, User 1 may have visited the homepage of the video-sharingservice provided by the video search system 302 and entered a firstquery of “football.” After seeing a list of video results responsive tothe first query, User 1 refines enters a second query of “hail Mary.”The video search system 302 returns, to User 1's computing device, asvideo content responsive to the second query, a second list of videosearch results, from which User 1 selects a first video to watch. User1's computing device can generate a presentation time report that issent to the video search system 302, including information thatidentifies the first video, information that identifies User 1 (e.g.,User 1's unique ID), information that identifies how long User 1 viewedthe first video, and information that identifies the first and secondqueries. The video search system 302 can then process and store all orsome of the information from the report in the presentation time datarepository 308, the behavioral data repository 322, or both. As such,the video search system 302 can correlate the presentation time for thefirst video with a viewer category associated with the first query, thesecond query, or both (or keywords extracted from the first and/orsecond queries). Other examples of behavioral characteristics includeviewer navigation data, which identifies one or more web pages that theuser visited in a session that led the viewer to a given video; viewhistory data, which indicates one or more other videos a user viewed ina presentation session in which the user also viewed a particular videothat is the subject of certain presentation time data; click data orconversion data of content items (e.g., ads) that the user selected inconnection with viewing a particular video. In some implementations, theviewer profile manager 318 may store data that identifies one or morecontent targeting parameters that were used to select video content orother content to serve to a viewer. The viewer profile manager 318, thepresentation time modeling apparatus 306, or both, may correlatespecific presentation time segments with behavioral characteristics ofthe viewer that generated the presentation time. In someimplementations, the data managed by the demographic data repository 320and the behavioral data repository 322 may be stored in one or moredatabases on devices in one or more locations.

In some implementations, the viewer profile manager 318 may define aplurality of viewer categories by grouping viewers according to theircharacteristics, as indicated by the demographic data repository 320and/or the behavioral data repository 322. In some implementations, eachunique viewer is assigned to only one of the plurality of viewercategories (i.e., the respective sets of characteristics of the viewercategories is non-overlapping). As an example, four viewer categoriesmay be defined based on respective combinations of the age groupcharacteristic and the gender characteristic, where each characteristichas two possible alternative values. A first group may consist of maleviewers in an age range of 25-35, a second group may consist of femaleviewers in an age range of 25-35, a third group may consist of maleusers over age 35, and a fourth group may consist of female users overage 35. Of course, as the viewer profile manager 318 defines viewercategories based on increasing numbers of characteristics and/orincreasing numbers of possible values for those characteristics, thetotal number of viewer categories may increase rapidly. For example, bysegmenting users into one of 5 age groups (rather than 2), the number ofviewer categories may increase from 4 to 10. Generally, the viewercategories may be defined as coarsely or granularly as needed in lightof the available viewer data. For example, combinations of tens orhundreds of characteristics may be used to define very granular viewercategories, or combinations of just a few characteristics may be used todefine coarser viewer categories.

In some implementations, the viewer profile manager 318 may define aplurality of viewer categories by grouping viewers according to theircharacteristics, where at least some of the viewer categories partiallyoverlap each other. As such, the viewer profile manager 318 may assign asingle viewer to two or more partially overlapping viewer categories asappropriate. For example, a first viewer category may consist of viewersthat (1) arrived at a video by submitting a search query having a firstkeyword and (2) are in the age range of 21-24. A second viewer categorymay consist of viewers that (1) arrived at a video by submitting asearch query having the first keyword and (2) are female. Therefore,User 3, a female viewer in the age range of 21-24 who, in this example,arrived at a video by submitting a search query having the firstkeyword, fits into both the first and second viewer categories becausethey are not mutually exclusive categories.

Viewer characteristics and other viewer profile information may beobtained by any of various techniques or combinations of techniques. Insome implementations, viewers may maintain accounts with the videosearch system 302, and users may voluntarily provide demographicinformation to the video search system 302 as part of their accountdata. In some implementations, viewer information may be derived fromcommunications received from viewers' computing devices, includingrequests for video content and presentation time reports sent fromviewers' computing devices. For example, location data may be includedor derived from messages received from viewer's computing devices, andbased on the location data a geographic location can be correlated witha viewer. In some implementations, the video search system 302 mayobtain viewer information from external sources other than the viewers'computing devices themselves. For instance, the video search system 302may obtain social data from social networks or may otherwise determineinformation about viewers from web pages or other publicly availabledocuments on the Internet.

In situations in which the systems and other techniques discussed herecollect personal information about users (e.g., viewers), or may makeuse of personal information, the users may be provided with anopportunity to control whether programs or features collect userinformation (e.g., information about a user's presentation time, socialnetwork, social actions or activities, profession, a user's preferences,a user's search history, a user's navigation history, or a user'scurrent location), or to control whether and/or how to receive contentfrom the video server that may be more relevant to the user. Inaddition, certain data may be treated in one or more ways before it isstored or used, so that personally identifiable information is removed.For example, a user's identity may be treated so that no personallyidentifiable information can be determined for the user, or a user'sgeographic location may be generalized where location information isobtained (such as to a city, ZIP code, or state level), so that aparticular location of a user cannot be determined. Thus, the user mayhave control over how information is collected about the user and usedby the system.

Referring again to the presentation time modeling apparatus 306 of thevideo search system 302, the presentation time modeling apparatus 306may generate, store, and maintain (e.g., update) one or morepresentation time models 312 based on video presentation time data, asindicated by the presentation time data repository 308, and based oncharacteristics of viewers whose activities produced the presentationtime data, as indicated by the viewer profile manager 318. Generally,the presentation time models 312 store data that indicate how longvarious groups of viewers were presented individual videos or groups ofvideos. In some implementations, the presentation time modelingapparatus 306 generates the presentation time models 312 by grouping thepresentation time for respective videos or groups of videos based oncharacteristics of the viewers of the respective videos or groups ofvideos. In some implementations, the presentation time modelingapparatus 306 identifies appropriate viewer groups from the viewerprofile manager 318. Therefore, the viewer groups employed by thepresentation time modeling apparatus 306 may correspond to the viewercategories defined by the viewer profile manager 318, such that thepresentation time models 312 indicate how long various viewers withineach of the viewer categories were presented individual videos or groupsof videos. For example, the presentation time data repository 308 mayshow that, over a certain period of time (e.g., an hour, a day, a week,or a month) 5,000 unique viewers were presented a particular video foran accumulated presentation time of 8 hours among all the viewers. Thepresentation time models 312 may, in turn, specify a distribution of thepresentation time for the particular video among various viewercategories, as indicated by the viewer profile manager 318. Forinstance, the presentation time model 312 may indicate that 37 minutesof the 8 hours of total presentation time are associated with maleviewers located in urban geographic areas, 4.5 hours of the totalpresentation time are associated with female viewers in rural geographicareas, 70 minutes of the total presentation time are associated withmale viewers in rural geographic areas, and the balance of the 8 hoursof presentation time is associated with associated with female viewerslocated in urban geographic areas. The presentation time model 312 thusindicates for the particular video relative interests in the video bydifferent categories of viewers as indicated by the relativepresentation times of the video by viewers in each category. Similarly,the presentation time modeling apparatus 306 may boost the presentationtime of viewers who have achieved a certain status on the video-sharingservice or a related service (e.g., a social network) as a reward tothose viewers for achieving the status or because the status signifies alevel of trust in the viewers' viewing habits. For example, thepresentation times of registered members of the video-sharing servicemay be boosted relative to the presentation times of non-registeredviewers.

As previously described, the viewer profile manager 318 can in someimplementations define partially overlapping viewer categories such thata given viewer can belong to multiple different viewer categories. Insuch implementations, the presentation time modeling apparatus 306 mayapportion the viewer's presentation time according to among each of theviewer categories to which the viewer belongs according to variouscriteria. For example, the presentation time modeling apparatus 306 mayassign 40-percent of a viewer's presentation time to a first viewercategory to which the user belongs, and can assign the remaining60-percent of the viewer's presentation time to a second viewer categoryto which the user belongs. In some implementations, the apportionment ofa viewer's presentation time among each applicable viewer category canbe based on scores assigned to the applicable viewer category. Thescores may reflect, for example, the relative values of each viewercategory to the video search system 302. For example, a given viewer maybelong to both first and second viewer categories, to which thepresentation time modeling apparatus 306 has assigned scores of 5 and10, respectively. Therefore, according to the viewer category scores, ⅓of the viewer's presentation time may be apportioned to the first viewercategory and ⅔ of the viewer's presentation time may be apportioned tothe second viewer category. In some implementations, the apportionmentof a viewer's presentation time among each applicable viewer categorycan be based on other information about the viewer or about the datathat was used to classify a user into one or more viewer categories. Forexample, the viewer profile manager 318 may process data about a viewerand may determine that there is a 75-percent likelihood that the vieweris male and a 25-percent likelihood that the viewer is female. Becausethe viewer profile manager 318 does not have complete confidence in theviewer's gender classification, 75-percent of the viewer's presentationtime may be assigned to a viewer category defined at least in part by amale characteristic, and 25-percent of the viewer's presentation timemay be assigned to a viewer category defined at least in part by afemale characteristic.

In some instances, it may be useful to keep track of viewer'spresentation times with respect to groups of videos rather than or inaddition to individual videos. As such, the presentation time modelingapparatus 306 may, in some implementations, determine presentation timemodels 312 that indicate, for each of multiple groups of videos, adistribution of presentation times of the video among various viewercategories. For example, the presentation time modeling apparatus 306may analyze data from the presentation time data repository 308 todetermine total presentation times of videos within respective groups ofvideos over a period of time by a population of viewers. The modelingapparatus can then group the presentation time for each group of videoby viewer category to generate presentation time distributions. Thepresentation time modeling apparatus 306 may group videos according tovarious criteria, as indicated by the video storage system 304. Forexample, videos may be grouped by creator, by channel, by age (e.g.,amount of time since a video was submitted for distribution on thevideo-sharing service), by genre (e.g., product reviews, music videos,animation, television shows, action, comedy, horror, children's videos),by popularity (e.g., total number of views), or a combination of two ormore of these. By determining presentation time distributions for groupsof videos, the video content selector 316 can, in some implementations,more readily determine video content to serve to a viewer's computingdevice in response to a request for video content by selecting videosfrom within one or more groups of videos that have relatively highpresentation times by viewers that have characteristics that matchcharacteristics of the viewer to whom the selected video content is tobe presented.

As the video search system 302 may constantly collect new presentationtime data from viewers, the presentation time modeling apparatus 306 maybe configured to update or regenerate the presentation time models 312(and the creator performance models 314) on a continuous or periodicbasis. In some implementations, the models 312, 314 may be maintainedbased on a rolling window of presentation time data. For example, onceeach day the presentation time modeling apparatus 306 may update themodels 312, 314 based on presentation time that occurred in the past 7days. Every day, then, the models 312, 314 can be updated to incorporatepresentation time data from a most recent day and to discardpresentation time data more than a week old. In some implementations,the presentation time modeling apparatus 306 can update the models 312,314 with a completely fresh set of data (e.g., every week the models maybe regenerated using data from only the most recent week). In someimplementations, the presentation time modeling apparatus 306 may updatethe models 312, 314 from time to time to incorporate a most recentlycollected set of presentation time data without discarding olderpresentation time data.

The presentation time models 312 may organize the groupings of videopresentation time information in various ways. In some implementations,each respective video or group of videos can be correlated with aplurality of values that respectively indicate the total (accumulated)presentation time of the respective video or group of videos by viewersin a respective one of a plurality of viewer categories. In someimplementations, the presentation time models 312 may indicate theconverse. Namely, for each of a plurality of viewer categories,presentation times of viewers within the respective viewer category maybe distributed among a set of videos or groups of videos.

In some implementations, the presentation time models 312 can indicate,for each video or group of videos served by the video search system 302over a period of time, a distribution of presentation times for therespective video or group of videos among each of a plurality of viewercategories. If no viewers have viewed a given video within a particularcategory during that period of time, the presentation time assigned tothat category may be null (zero). In some implementations, thepresentation time models 312 may identify actual presentation timetotals for each viewer category (e.g., viewers in a first category werepresented the video for a total of 132 minutes, while viewers in asecond category were presented the video for a total of 61 minutes). Insome implementations, the presentation time models 312 may identifyrelative presentation time totals for each viewer category (e.g.,68-percent of the presentation time for the video was by viewers in thefirst category, while 32-percent of the presentation time for the videowas by viewers in the second category). In some implementations, thevideo search system 302 may value certain viewers' presentation timemore than others, and therefore the presentation time modeling apparatus306 may weigh the presentation time of individual viewers or groups ofviewers when determining the presentation time distributions for thepresentation time models 312. For example, a celebrity or an expert in afield that is the subject of a video or group of videos may have theiractual presentation times tripled for the video or group of videos, orother users' presentation times may be devalued.

In some implementations, the presentation time modeling apparatus 306can use video presentation time data to determine one or more creatorperformance models 314. Generally, the creator performance models 314identify creator performance scores (i.e., scores for parties that havesubmitted videos to the video-sharing service for distribution). Thecreator performance scores can be determined by the presentation timemodeling apparatus 306 based on how long various categories of viewershave viewed the creators' videos. In this way, the video search system302 can leverage presentation time information as a metric for assessingthe performance of creators on the video-sharing service. Moreover, andas further described with respect to the video content selector 316, thecreator performance scores can be used in some implementations as aheuristic for ranking and determining video content to serve to viewers.In some implementations, the video search system 302 may also determinehow to allocate resources to creators based at least in part on theperformance scores resources to distribute to creators based on theperformance scores.

In some implementations, the presentation time modeling apparatus 306may determine creator performance scores as follows. First, thepresentation time modeling apparatus 306 accesses from the presentationtime data repository 308 information about how long viewers werepresented various videos over a period of time. The presentation timemodeling apparatus 306 then identifies from the viewer profile manager318 a set of one or more viewer categories that will form the basis ofthe creator performance scores. In some implementations, the identifiedset of viewer categories may be a complete set of viewer categories thatencompasses all viewers, or the identified set of viewer categories maycomprise less than all of the viewer categories in the complete set. Forexample, if the complete set of viewer categories included (1) malesaged 46-55 (2) males aged 38-45, (3) females aged 46-55, and (4) femalesaged 38-45, then the presentation time modeling apparatus 306 coulddetermine the creator performance scores based on the presentation timesof viewers within all four categories (the complete set), or based onthe presentation times of viewers within fewer than all four categories.

The presentation time modeling apparatus 306 then generates groups ofpresentation time for each of the identified viewer categories byassigning respective pieces of presentation time to appropriate ones ofthe viewer categories based on the characteristics of the viewers whoseviews resulted in the respective presentation times. For example, thepresentation times of one or more videos by one or more viewers whobelong to a first viewer category may be assigned to a presentation timegroup for the first viewer category, the presentation times of one ormore videos by one or more viewers who belong to a second viewercategory may be assigned to a presentation time group for the secondviewer category, and so on. Based on the groupings, the presentationtime modeling apparatus 306 then accumulates the presentation times ineach viewer category to determine a total presentation time thatindicates, for each viewer category, a total amount of time that viewerswithin the category were presented with videos over a defined timeinterval (e.g., a day, a week, a month).

The presentation time modeling apparatus 306 also breaks down the totalpresentation time in each viewer category by creator. That is, in eachviewer category, the presentation time modeling apparatus 306 identifiesall the creators of the videos that were presented by viewers within thecategory, and determines for each of the identified creators how much ofthe total presentation time for the category was presentation time ofvideos of the respective creator. As an example, the presentation timemodeling apparatus 306 may determine that, over the course of a month,viewers aged 21 and under were presented a total of 2,000 hours ofvideo, while viewers aged 22+ were presented a total of 4,000 hours ofvideo over that month. Moreover, the total presentation time of multiplevideos distributed by a first creator on the video-sharing service overthat month by viewers aged 21 may be determined as 130 hours. The totalpresentation time of the multiple videos distributed by the firstcreator on the video-sharing service over that month by viewers aged 22+may be determined as 25 hours. Thus, the first creator's videos can beseen to contribute to a greater share of the total presentation time forthe 21 category than the 22+ category.

Further in the process of determining creator performance scores, thepresentation time modeling apparatus 306 can identify scores for each ofthe viewer categories that the presentation time modeling apparatus 306can use in determining scaling factors for videos. Presentation timesfrom different viewer categories may be scored differently from eachother, for example, to reward creators whose videos generate morepresentation time from viewer categories that the video-sharing servicedeems more valuable than other categories. Thus, if the video-sharingservice targets video content to particular demographics of viewers,creators can receive more credit for presentation time generated byviewers within the targeted demographics than for presentation time byviewers in other demographics.

Using the viewer category scores and the presentation times associatedwith the various viewer categories, the presentation time modelingapparatus 306 can compute the creator performance scores. In someimplementations, the creator performance score for a given creator canbe computed by (1) determining, for each respective viewer category, theproduct of the (i) share of the total presentation time for therespective viewer category that is attributable to videos associatedwith the given creator and (ii) the viewer category score for therespective viewer category, and (2) taking the sum of the productsacross all the viewer categories. For example, consider a scenario inwhich viewers in a first category were presented 100 minutes of videosover a period of time and viewers in a second category were presented200 minutes of videos over the same period of time. The share of thepresentation time attributable to videos of a particular creator byviewers in the first category is 20 minutes and the share of thepresentation time attributable to videos of the same creator by viewersin the second category is also 20 minutes. The viewer category score forthe first video is 50 and the viewer category score for the second videois 250. The creator performance score for the particular creator can becalculated as (20/100)*(50)±(20/200)*(250)=35. In some implementations,the video-sharing service can use the creator performance scores as amodel or heuristic for distributing resources (e.g., points, incentives,membership status, access to creative tools, or revenue) to thecreators.

The video content selector 316 is operable to select video content toserve to various computing devices in response to requests for videocontent. Generally, the video content selector 316 can select videocontent to serve based on presentation time information, as indicated bythe presentation time models 312, based on creator performance scores,as indicated by the creator performance models 314, or both. In responseto a request from a particular viewer, the video content selector 316may select video content for one or more videos to serve to theparticular viewer's computing device based on identifying thathistorically the one or more videos, or other videos that are similar tothe one or more videos, were presented for relatively long times tovarious viewers having characteristics that match or are similar tocharacteristics of the viewer for whom the video content is targeted.For example, the video content selector 316 may identify, from therequest manager 326, that a user who submitted a request for videocontent is a male, 21, from Albuquerque, N. Mex.

The video content selector 316 can then query the presentation timemodels 312 to identify videos that the presentation time data indicateswere preferred (e.g., presented for longer times) by other users thatmatch the same profile of the requesting user. Generally, videos havinghigher presentation times by matching or similar viewers are more likelyto be selected in response to a request than are videos having lowerpresentation times by matching or similar viewers. Continuing thepreceding example, the video content selector 316 may rank a pluralityof videos that are determined to be relevant to the request from themale, 21, from Albuquerque.

The videos may be ranked based on multiple heuristics, including howclosely the subject matter of candidate videos matches one or moretopics of the request, presentation time heuristics, and/or creatorperformance heuristics. With respect to the presentation timeheuristics, candidate videos can be promoted in the ranking the longerthat the videos were presented to 21-year-old men from Albuquerque or byviewers in similar demographics. With respect to the creator performanceheuristics, candidate videos by creators having higher performancescores may be promoted in the ranking. The video content selector 316can then select video content for one or more of the top-rankedcandidate videos to serve to the requesting user's computing device. Insome implementations, the served content may be the digital videosthemselves. In some implementations, the served content may not includethe digital videos themselves, but may include references to the digitalvideos (e.g., search results that include a title, description, and/orrepresentative image of the selected videos).

In some implementations, the video content selector 316 may applyback-off techniques to identify viewer characteristics (and hence viewercategories) that are similar to characteristics of a user that hasrequested video content. For example, rather than limiting the analysisof presentation time by 21-year-old male viewers in Albuquerque, N.Mex., the video content selector 316 may query the presentation timemodels 312 to identify presentation times of videos by 21-year-old maleviewers in the entire southwest United States. By expanding the relevantgeographic area (e.g., by backing-off), more data points can be analyzedto determine more reliable results.

Turning to FIG. 4, a flowchart is shown of an example process 400 inwhich videos are presented and scaling factors are used to determinescaled presentation times for videos. In some implementations, theprocess 400 may be carried out by the systems and devices discussedthroughout this document, including by various components of the videosearch system 302 of FIG. 3, and specifically the presentation scoringapparatus 305.

The process can begin at stage 402, when various videos are identifiedthat have been presented at various different client devices that areremotely located relative to one or more computing devices thatdistribute the various videos for an online video distribution service.For example, the presentation scoring apparatus 305, using at least thepresentation time data repository 308, can identify presented videos104.

At stage 404, for each of the various videos, session start data of auser within the online video distribution service is identified. Thesession start data specifies a lead video that initiated videopresentation to the user during a given presentation session. Forexample, the presentation scoring apparatus 305 can determine the videos104 that are associated with the presentation session 102. The leadvideo identifier/classifier 307 can identify the presented video A 104 aas the lead video, as opposed to other ones of the presented videos 104that follow the lead video. Identification of the lead video can be madeby evaluating start and stop times of videos, such as using informationfrom the presentation time data repository 308.

At stage 406, each lead video is processed. For example, thepresentation scoring apparatus 305 can process all known lead videos,including the presented video A 104 a, using the following stages408-414.

At stage 408, various presentation times of the lead video over multipleuser sessions are identified. As an example, the presentation scoringapparatus 305, using information from the presentation time datarepository 308, can identify the presentation times 106 applicable toeach of the presented videos 104 for the presentation session 102, aswell as presentation times for lead videos of other presentationsessions.

At stage 410, a scaled presentation time for each lead videopresentation is obtained, including applying a scaling factor to thedetermined presentation time for the lead video presentation. Thescaling factor engine 309, for example, can determine the scaledpresentation times 110 associated with each of the presented videos 104,such as multiplying the presentation times 106 by the correspondingscaling factors 108 for a given video. The presentation time 106 (e.g.,five minutes) for presented video 104 a, for example, being a lead video(as being referred by through a social messaging system and being thefirst video in the presentation session 102), can be scaled using ascaling factor 108 of 2. Further, the presentation time 106 (e.g., fiveminutes) for the presented video B 104 b, for example, having the sameauthor as (and presented immediately after) the lead video, can also bescaled with a scaling factor 108 of 2 (or may have a different scalingfactor). While the smallest scaling factor 108 presented in FIG. 1 isone (e.g., indicating no additional boost in rank is to be applied orgiven to a particular video), scaling factor 108 values of less than onecan be used, such as to reduce the rank (and likely appearance as arecommendation to users) if the video has been determined to be not ofinterest to the user (or generally to other users).

At stage 412, the user sessions for which the lead video initiatedpresentation of videos to a user are identified. For example, once videoA 104 a has been identified as a lead video, the presentation session102 can be identified as containing the lead video. Further, otherpresentation sessions for other lead videos can be identified.

At stage 414, an aggregate video presentation time attributable to thelead video is determined based on the scaled presentation time of thelead video for each of the identified user sessions and a totalpresentation time of other videos during the identified user sessions.For example, the aggregate video presentation time 111 of the presentedvideos 104 can be computed by summing the scaled presentation times 110for the presentation session 102.

At stage 416, for each given video among the various videos, apresentation score 112 is generated based on an amount of presentationtime of the given video relative to a sum of the aggregate videopresentation times 111 a for the lead videos. For example, the scalingfactor engine 309 can calculate the presentation scores 112 for each ofthe presented videos 104. In this example, in order to show thecalculations used, the generated presentation scores 112 are based onthe aggregate video presentation time 111 associated with the presentedvideos 104 in the presentation session 102.

A lead video presentation score 112 a for each lead video is generatedbased on the scaled presentation times of a respective lead videorelative to the sum of the aggregate video presentation times 111 a forthe lead videos. For example, the scaling factor engine 309 cancalculate a respective lead video presentation score 112 a for each ofthe presented videos 104 based on the respective scaled presentationstime 110 relative to the aggregate video presentation times 111 a.

In some implementations, presentation scores 112 can be determined oradjusted so that presentation times 106 dominate less of eachpresentation score 112 and scaling factors 108 provide a largercontribution. For example, instead of a mathematical product of thepresentation time 106 and the scaling factor 108, the presentation score112 can be determined using a function that uses a square of the scalingfactor 108 and/or a normalized presentation time 106, or larger scalingfactors 108 can be used. Other ways of determining presentation scorescan be used.

At stage 418, the various videos are ranked based on the presentationscores. For example, the videos 104 a-104 c can be ranked based on thepresentation scores. In another example, the videos 104 a-104 c can beranked based on the scaling factor, such as to produce a higher rankingfor the presented videos 104 a and 104 b (e.g., referred videos andvideos of the same author) than for the video 104 c (e.g., simply avideo site-recommended video).

At stage 420, a user interface of the online video distribution serviceis updated to present at least a portion of the ranked videos at aclient device according to the ranking. For example, in a user interfaceof the online video distribution service, such as at a video-sharingwebsite, the videos 104 a-104 c can be listed before other lower-rankedvideos, such as presented video C 104 c.

In some implementations, the process 400 can further include applying ahigher scaling factor to presentation times for remotely initiated videopresentations of a lead video. For example, higher scaling factors canbe applied when the user is brought to the online video distributionservice from a location (e.g., a social messaging system) that is remotefrom the online video distribution service. Determining and applyinghigher scaling factors can include, for each lead video specified by thepresentation session start data for the various videos, classifying thelead video as either an in-service initiated video presentation (e.g.,at the video-sharing website) or a remotely initiated video presentation(e.g., initiated by clicking on a link provided by a friend in a socialmessaging system). Applying a scaling factor to the determinedpresentation time for the lead video presentation includes applying afirst (e.g., lower) scaling factor to the determined presentation timefor in-service initiated video presentations of a lead video, andapplying a second (e.g., higher) scaling factor to the determinedpresentation time for remotely initiated video presentations of a leadvideo.

In some implementations, the process 400 can further include identifyingremotely initiated video presentations of a lead video based on referrerinformation included in a request to present the lead video. Forexample, different referrers can result in using different scalingfactors, such as videos for a video creator having a greater followingreceiving a higher scaling factor than videos of other less-followedcreators. In some implementations, referrer information can be includedin the URL that is used to initiate the presentation session on theonline video distribution service. For example, the referrer informationcan specify one of a third-party website that directed the user to theonline video distribution service, a third-party native application thatdirected the user to the online video distribution service, or sharedlink that directed the user to the online video distribution service.

In some implementations, the process 400 can further include applyingscaling factors to different presentations differently based on howindicative of the video the query is. For example, search queries can beidentified that have resulted in a given lead video being identified tovarious users in search results. For each of the identified searchqueries, for example, a portion of the various users that initiatedpresentation of the given lead video through interaction with the searchresults can be determined. Based on the determined portions, searchscaling factors can be determined that are applied to the determinedpresentation time of the lead video for the presentations of the givenlead video that was initiated through user interaction with the searchresults that identified the given lead video.

In some implementations, the process 400 can further include identifyinga creator that supplies one or more of the various videos. For example,a creator score can be generated for the creator based on thepresentation score of the one or more of the various videos provided bythe creator, and the creator can be ranked among other creators based,at least in part, on the creator score. In some implementations, creatorscores can be used instead of or in addition to scaling factors 108.

In some implementations, the process 400 can further includedistributing, to the creator, a portion of proceeds attributable to theone or more of the various videos supplied by the creator based on thepresentation scores of the one or more of the various videos. Forexample, instead of compensating a creator of a video based solely orprimarily on the length of a video (e.g., relative to the length of thepresentation and using proceeds from the session), the compensation canbe based, at least in part, on the compensation score (e.g., determinedfrom scaling factors, based on the source of the video, such as whetherthe video is a lead video).

FIG. 5 shows an example of a computing device 500 and a mobile computingdevice that may be used to implement the computer-implemented methodsand other techniques described herein. The computing device 500 isintended to represent various forms of digital computers, such aslaptops, desktops, workstations, personal digital assistants, servers,blade servers, mainframes, and other appropriate computers. The mobilecomputing device is intended to represent various forms of mobiledevices, such as personal digital assistants, cellular telephones,smart-phones, and other similar computing devices. The components shownhere, their connections and relationships, and their functions, aremeant to be exemplary only, and are not meant to limit implementationsof the inventions described and/or claimed in this document.

The computing device 500 includes a processor 502, a memory 504, astorage device 506, a high-speed interface 508 connecting to the memory504 and multiple high-speed expansion ports 510, and a low-speedinterface 512 connecting to a low-speed expansion port 514 and thestorage device 506. Each of the processor 502, the memory 504, thestorage device 506, the high-speed interface 508, the high-speedexpansion ports 510, and the low-speed interface 512, are interconnectedusing various busses, and may be mounted on a common motherboard or inother manners as appropriate. The processor 502 can process instructionsfor execution within the computing device 500, including instructionsstored in the memory 504 or on the storage device 506 to displaygraphical information for a GUI on an external input/output device, suchas a display 516 coupled to the high-speed interface 508. In otherimplementations, multiple processors and/or multiple buses may be used,as appropriate, along with multiple memories and types of memory. Also,multiple computing devices may be connected, with each device providingportions of the necessary operations (e.g., as a server bank, a group ofblade servers, or a multi-processor system).

The memory 504 stores information within the computing device 500. Insome implementations, the memory 504 is a volatile memory unit or units.In some implementations, the memory 504 is a non-volatile memory unit orunits. The memory 504 may also be another form of computer-readablemedium, such as a magnetic or optical disk.

The storage device 506 is capable of providing mass storage for thecomputing device 500. In some implementations, the storage device 506may be or contain a computer-readable medium, such as a floppy diskdevice, a hard disk device, an optical disk device, or a tape device, aflash memory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. The computer program product may also containinstructions that, when executed, perform one or more methods, such asthose described above. The computer program product can also be tangiblyembodied in a computer- or machine-readable medium, such as the memory504, the storage device 506, or memory on the processor 502.

The high-speed interface 508 manages bandwidth-intensive operations forthe computing device 500, while the low-speed interface 512 manageslower bandwidth-intensive operations. Such allocation of functions isexemplary only. In some implementations, the high-speed interface 508 iscoupled to the memory 504, the display 516 (e.g., through a graphicsprocessor or accelerator), and to the high-speed expansion ports 510,which may accept various expansion cards (not shown). In theimplementation, the low-speed interface 512 is coupled to the storagedevice 506 and the low-speed expansion port 514. The low-speed expansionport 514, which may include various communication ports (e.g., USB,Bluetooth, Ethernet, wireless Ethernet) may be coupled to one or moreinput/output devices, such as a keyboard, a pointing device, a scanner,or a networking device such as a switch or router, e.g., through anetwork adapter.

The computing device 500 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 520, or multiple times in a group of such servers. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 522. It may also be implemented as part of a rack server system524. Alternatively, components from the computing device 500 may becombined with other components in a mobile device (not shown), such as amobile computing device 550. Each of such devices may contain one ormore of the computing device 500 and the mobile computing device 550,and an entire system may be made up of multiple computing devicescommunicating with each other.

The mobile computing device 550 includes a processor 552, a memory 564,an input/output device such as a display 554, a communication interface566, and a transceiver 568, among other components. The mobile computingdevice 550 may also be provided with a storage device, such as amicro-drive or other device, to provide additional storage. Each of theprocessor 552, the memory 564, the display 554, the communicationinterface 566, and the transceiver 568, are interconnected using variousbuses, and several of the components may be mounted on a commonmotherboard or in other manners as appropriate.

The processor 552 can execute instructions within the mobile computingdevice 550, including instructions stored in the memory 564. Theprocessor 552 may be implemented as a chipset of chips that includeseparate and multiple analog and digital processors. The processor 552may provide, for example, for coordination of the other components ofthe mobile computing device 550, such as control of user interfaces,applications run by the mobile computing device 550, and wirelesscommunication by the mobile computing device 550.

The processor 552 may communicate with a user through a controlinterface 558 and a display interface 556 coupled to the display 554.The display 554 may be, for example, a TFT (Thin-Film-Transistor LiquidCrystal Display) display or an OLED (Organic Light Emitting Diode)display, or other appropriate display technology. The display interface556 may comprise appropriate circuitry for driving the display 554 topresent graphical and other information to a user. The control interface558 may receive commands from a user and convert them for submission tothe processor 552. In addition, an external interface 562 may providecommunication with the processor 552, so as to enable near areacommunication of the mobile computing device 550 with other devices. Theexternal interface 562 may provide, for example, for wired communicationin some implementations, or for wireless communication in otherimplementations, and multiple interfaces may also be used.

The memory 564 stores information within the mobile computing device550. The memory 564 can be implemented as one or more of acomputer-readable medium or media, a volatile memory unit or units, or anon-volatile memory unit or units. An expansion memory 574 may also beprovided and connected to the mobile computing device 550 through anexpansion interface 572, which may include, for example, a SIMM (SingleIn Line Memory Module) card interface. The expansion memory 574 mayprovide extra storage space for the mobile computing device 550, or mayalso store applications or other information for the mobile computingdevice 550. Specifically, the expansion memory 574 may includeinstructions to carry out or supplement the processes described above,and may include secure information also. Thus, for example, theexpansion memory 574 may be provide as a security module for the mobilecomputing device 550, and may be programmed with instructions thatpermit secure use of the mobile computing device 550. In addition,secure applications may be provided via the SIMM cards, along withadditional information, such as placing identifying information on theSIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory(non-volatile random access memory), as discussed below. The computerprogram product contains instructions that, when executed, perform oneor more methods, such as those described above. The computer programproduct can be a computer- or machine-readable medium, such as thememory 564, the expansion memory 574, or memory on the processor 552. Insome implementations, the computer program product can be received in apropagated signal, for example, over the transceiver 568 or the externalinterface 562.

The mobile computing device 550 may communicate wirelessly through thecommunication interface 566, which may include digital signal processingcircuitry where necessary. The communication interface 566 may providefor communications under various modes or protocols, such as GSM voicecalls (Global System for Mobile communications), SMS (Short MessageService), EMS (Enhanced Messaging Service), or MMS messaging (MultimediaMessaging Service), CDMA (code division multiple access), TDMA (timedivision multiple access), PDC (Personal Digital Cellular), WCDMA(Wideband Code Division Multiple Access), CDMA2000, or GPRS (GeneralPacket Radio Service), among others. Such communication may occur, forexample, through the transceiver 568 using a radio-frequency. Inaddition, short-range communication may occur, such as using aBluetooth, WiFi, or other such transceiver (not shown). In addition, aGPS (Global Positioning System) receiver module 570 may provideadditional navigation- and location-related wireless data to the mobilecomputing device 550, which may be used as appropriate by applicationsrunning on the mobile computing device 550.

The mobile computing device 550 may also communicate audibly using anaudio codec 560, which may receive spoken information from a user andconvert it to usable digital information. The audio codec 560 maylikewise generate audible sound for a user, such as through a speaker,e.g., in a handset of the mobile computing device 550. Such sound mayinclude sound from voice telephone calls, may include recorded sound(e.g., voice messages, music files, etc.) and may also include soundgenerated by applications operating on the mobile computing device 550.

The mobile computing device 550 may be implemented in a number ofdifferent forms, as shown in the figure. For example, it may beimplemented as a cellular telephone 580. It may also be implemented aspart of a smart-phone 582, personal digital assistant, or other similarmobile device.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms machine-readable medium andcomputer-readable medium refer to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term machine-readable signal refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (LAN), a wide area network (WAN), and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

In situations in which the systems, methods, devices, and othertechniques here collect personal information (e.g., context data) aboutusers, or may make use of personal information, the users may beprovided with an opportunity to control whether programs or featurescollect user information (e.g., information about a user's socialnetwork, social actions or activities, profession, a user's preferences,or a user's current location), or to control whether and/or how toreceive content from the content server that may be more relevant to theuser. In addition, certain data may be treated in one or more waysbefore it is stored or used, so that personally identifiable informationis removed. For example, a user's identity may be treated so that nopersonally identifiable information can be determined for the user, or auser's geographic location may be generalized where location informationis obtained (such as to a city, ZIP code, or state level), so that aparticular location of a user cannot be determined. Thus, the user mayhave control over how information is collected about the user and usedby a content server.

Although various implementations have been described in detail above,other modifications are possible. In addition, the logic flows depictedin the figures do not require the particular order shown, or sequentialorder, to achieve desirable results. In addition, other steps may beprovided, or steps may be eliminated, from the described flows, andother components may be added to, or removed from, the describedsystems. Accordingly, other implementations are within the scope of thefollowing claims.

What is claimed is:
 1. A system comprising: one or more processors; andone or more memory devices including instructions that, when executed,cause the one or more processors to perform operations comprising:identifying, for a given video, session start data specifying that thegiven video was a lead video that initiated video presentation to one ormore users during multiple user sessions; for each of at least some usersessions among the multiple user sessions: classifying the lead video asone of an in-service initiated video presentation or a remotelyinitiated video presentation; and applying a scaling factor to one ormore presentation times for remotely initiated video presentations ofthe lead video, wherein the scaling factor increases a valuecorresponding to remotely initiated video presentations relative toin-service video presentations; determining a video presentation timeattributable to the given video as a lead video based on a totalpresentation time of other videos during the multiple user sessions thatwere initiated with the given video as the lead video; and generating apresentation score for the given video based on the video presentationtime attributable to the given video as the lead video.
 2. The system ofclaim 1, the operations further comprising: ranking the given videoamong other videos based on the presentation score; and updating a userinterface to present a highest ranked portion of the ranked videos at aclient device according to the ranking.
 3. The system of claim 2,wherein ranking the given video based on the presentation scorecomprises ranking the given video to reduce a number of videos previewedby the user before the given video is presented to the user based on thefirst presentation score and a friend of the user identifying the givenvideo for the user.
 4. The system of claim 1, wherein applying a scalingfactor to one or more presentation times for remotely initiated videopresentations of the lead video comprises: applying a first scalingfactor to one or more presentation times for in-service initiated videopresentations of the lead video; and applying a second scaling factor toone or more presentation times for remotely initiated videopresentations of the lead video, wherein the first scaling factor islower than the second scaling factor.
 5. The system of claim 4, theoperations further comprising: identifying remotely initiated videopresentations of the lead video based on referrer information includedin a request to present the lead video, wherein the referrer informationspecifies one of a third-party website that directed a user to an onlinevideo distribution service, a third-party native application thatdirected a user to the online video distribution service, or shared linkthat directed a user to the online video distribution service;collecting timestamps indicating playback start times of the lead video;obtaining pings generated during playbacks of the lead video; anddetermining various presentation times for the playbacks of the leadvideo based on the timestamps and the pings.
 6. The system of claim 4,the operations further comprising: identifying search queries thatresulted in the lead video being identified to various users in searchresults; for each of the search queries, determining a portion of thevarious users that initiated presentation of the given video throughinteraction with the search results; and determining, based on thedetermined portions, search scaling factors to apply to the determinedpresentation time of the lead video for the presentations of the givenvideo that were initiated through user interaction with the searchresults that identified the given lead video.
 7. The system of claim 1,the operations further comprising: generating a creator score for acreator that supplies the video based on the presentation score; andranking the creator among other creators based, at least in part, on thecreator score.
 8. A method comprising: identifying, by one or moreprocessors and for a given video, session start data specifying that thegiven video was a lead video that initiated video presentation to one ormore users during multiple user sessions; for each of at least some usersessions among the multiple user sessions: classifying the lead video asone of an in-service initiated video presentation or a remotelyinitiated video presentation; and applying a scaling factor to one ormore presentation times for remotely initiated video presentations ofthe lead video, wherein the scaling factor increases a valuecorresponding to remotely initiated video presentations relative toin-service video presentations; determining, by the one or moreprocessors, a video presentation time attributable to the given video asa lead video based on a total presentation time of other videos duringthe multiple user sessions that were initiated with the given video asthe lead video; and generating, by the one or more processors, apresentation score for the given video based on the video presentationtime attributable to the given video as the lead video.
 9. The method ofclaim 8, further comprising: ranking the given video among other videosbased on the presentation score; and updating a user interface topresent a highest ranked portion of the ranked videos at a client deviceaccording to the ranking.
 10. The method of claim 9, wherein ranking thegiven video based on the presentation score comprises ranking the givenvideo to reduce a number of videos previewed by the user before thegiven video is presented to the user based on the first presentationscore and a friend of the user identifying the given video for the user.11. The method of claim 8, wherein applying a scaling factor to one ormore presentation times for remotely initiated video presentations ofthe lead video comprises: applying a first scaling factor to one or morepresentation times for in-service initiated video presentations of thelead video; and applying a second scaling factor to one or morepresentation times for remotely initiated video presentations of thelead video, wherein the first scaling factor is lower than the secondscaling factor.
 12. The method of claim 11, further comprising:identifying remotely initiated video presentations of the lead videobased on referrer information included in a request to present the leadvideo, wherein the referrer information specifies one of a third-partywebsite that directed a user to an online video distribution service, athird-party native application that directed a user to the online videodistribution service, or shared link that directed a user to the onlinevideo distribution service; collecting timestamps indicating playbackstart times of the lead video; obtaining pings generated duringplaybacks of the lead video; and determining various presentation timesfor the playbacks of the lead video based on the timestamps and thepings.
 13. The method of claim 11, further comprising: identifyingsearch queries that resulted in the lead video being identified tovarious users in search results; for each of the search queries,determining a portion of the various users that initiated presentationof the given video through interaction with the search results; anddetermining, based on the determined portions, search scaling factors toapply to the determined presentation time of the lead video for thepresentations of the given video that were initiated through userinteraction with the search results that identified the given leadvideo.
 14. The method of claim 13, further comprising: generating acreator score for a creator that supplies the given video based on thefirst presentation score and the second presentation score; and rankingthe creator among other creators based, at least in part, on the creatorscore.
 15. One or more non-transitory computer-readable media havinginstructions stored thereon that, when executed by one or moreprocessors, cause performance of operations comprising: identifying, fora given video, session start data specifying that the given video was alead video that initiated video presentation to one or more users duringmultiple user sessions; for each of at least some user sessions amongthe multiple user sessions: classifying the lead video as one of anin-service initiated video presentation or a remotely initiated videopresentation; and applying a scaling factor to one or more presentationtimes for remotely initiated video presentations of the lead video,wherein the scaling factor increases a value corresponding to remotelyinitiated video presentations relative to in-service videopresentations; determining a video presentation time attributable to thegiven video as a lead video based on a total presentation time of othervideos during the multiple user sessions that were initiated with thegiven video as the lead video; and generating a presentation score forthe given video based on the video presentation time attributable to thegiven video as the lead video.
 16. The one or more non-transitorycomputer-readable media of claim 15, the operations further comprising:ranking the given video among other videos based on the presentationscore; and updating a user interface to present a highest ranked portionof the ranked videos at a client device according to the ranking. 17.The one or more non-transitory computer-readable media of claim 16,wherein ranking the given video based on the presentation scorecomprises ranking the given video to reduce a number of videos previewedby the user before the given video is presented to the user based on thefirst presentation score and a friend of the user identifying the givenvideo for the user.
 18. The one or more non-transitory computer-readablemedia of claim 15, wherein applying a scaling factor to one or morepresentation times for remotely initiated video presentations of thelead video comprises: applying a first scaling factor to one or morepresentation times for in-service initiated video presentations of thelead video; and applying a second scaling factor to one or morepresentation times for remotely initiated video presentations of thelead video, wherein the first scaling factor is lower than the secondscaling factor.
 19. The one or more non-transitory computer-readablemedia of claim 18, the operations further comprising: identifyingremotely initiated video presentations of the lead video based onreferrer information included in a request to present the lead video,wherein the referrer information specifies one of a third-party websitethat directed a user to an online video distribution service, athird-party native application that directed a user to the online videodistribution service, or shared link that directed a user to the onlinevideo distribution service; collecting timestamps indicating playbackstart times of the lead video; obtaining pings generated duringplaybacks of the lead video; and determining various presentation timesfor the playbacks of the lead video based on the timestamps and thepings.
 20. The one or more non-transitory computer-readable media ofclaim 18, the operations further comprising: identifying search queriesthat resulted in the lead video being identified to various users insearch results; for each of the search queries, determining a portion ofthe various users that initiated presentation of the given video throughinteraction with the search results; and determining, based on thedetermined portions, search scaling factors to apply to the determinedpresentation time of the lead video for the presentations of the givenvideo that were initiated through user interaction with the searchresults that identified the given lead video.